Systems, methods and devices for use in assessing carcass grading

ABSTRACT

Methods, systems and devices are implemented in connection with measuring the relative content of intramuscular fat (IMF) in a portion of muscle tissue. Consistent with one such method a probe is presented to the portion of muscle tissue. The probe produces a response-provoking signal in the muscle tissue. A resulting signal is used to determine the relative content of IMF in the portion of muscle tissue as a function of the pressure being exerted between the probe and the portion.

RELATED DOCUMENTS

This patent document claims the benefit, under 35 U.S.C. §119(e), ofU.S. Provisional Patent Application Ser. No. 61/050,533 filed on May 5,2008, and entitled “SYSTEMS, METHODS AND DEVICES FOR USE IN ASSESSINGMUSCLE TISSUE QUALITY;” this patent document and the Appendices filed inthe underlying provisional application are fully incorporated herein byreference.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Subject matter described in this document is based upon work supportedby the Cooperative State Research, Education, and Extension Service,U.S. Department of Agriculture, under Agreement Nos. 2006-33610-16761and 2007-33610-18441. The U.S. government may have certain rights to theinvention.

FIELD OF THE INVENTION

The present invention relates to systems and methods for inspecting andmeasuring muscle tissue parameters, such as fat and lean content andquality of muscle tissue.

BACKGROUND

There are several attributes of muscle tissue quality that relate topalatability and consumer eating satisfaction. Assessments of suchqualities can be useful for a variety of food animals. Such assessmentscan also be useful in both live animals and animal carcasses. Forexample, one such important attributes is the amount of intramuscularfat (IMF) that exists in the longissimus dorsi muscle. Within the U.S.,the longissimus dorsi muscle or “loin” is a very high value part of thepork carcass. IMF in the pork loin adds significantly to flavor andjuiciness, traits that are highly related to eating satisfaction. Theamount of the IMF in the pork loin is governed by genetics, age of theanimal at time of harvest and to a lesser degree by other environmentalfactors and animal nutrition.

There is considerable variation in IMF from animal to animal or fromcarcass to carcass with mean values in the range of 2.0-2.5%. Carcasseswith less than 2.0% IMF can be undesirable. Carcasses with more than3.5% IMF are valued by high-end restaurant chefs that offer pork ontheir menus. Carcasses with more than 6% IMF are highly valued in someforeign markets, such as in Japan. Because of these markets differences,the ability to noninvasively measure the amount of IMF in the pork loinhas value to the pork packing plant as well as to other aspects of themuscle tissue-processing industry.

A significant challenge to measuring IMF in the packing plant is thespeed by which carcasses are processed. As an example, with many plantsrunning their chain speed at 1200 carcasses per hour, a carcass would bemeasured in less than 2 seconds if the carcass is going to be measuredduring the packing process. In addition, pork carcasses are notroutinely split anywhere along the loin that would expose the internaltissue for either a subjective or quantitative measure of the amount ofIMF in the lean tissue. Consequently, packing plants can benefit fromefficient and practical methods of noninvasively “looking” inside theloin muscle and determining the percentage of IMF as compared to theamount of lean tissue.

SUMMARY

The present invention is directed to systems and methods for inspectingaspects, such as content and quality of muscle tissue. These and otheraspects of the present invention are exemplified in a number ofillustrated implementations and applications, some of which are shown inthe figures and characterized in the claims section that follows.

Consistent with an embodiment of the present invention, a method isimplemented for measuring the relative content of intramuscular fat(IMF) in a portion of muscle tissue. A probe is presented to the portionof carcass skin covering subcutaneous fat and the muscle tissue. Theprobe produces a response-provoking signal in the muscle tissue. Aresulting signal is used to determine the relative content of IMF in theportion of muscle tissue as a function of the pressure being exertedbetween the probe and the portion.

Consistent with another embodiment of the present invention, a systemmeasures the relative content of intramuscular fat (IMF) in a portion ofmuscle tissue. A probe carries a response-provoking signal to theportion of muscle tissue. A pressure sensor senses pressure beingexerted between the probe and the portion. A data processor measures therelative content of IMF in the portion of muscle tissue. The relativecontent of IMF is determined as a function of the response-provokingsignal and the sensed pressure.

In a specific embodiment, the resulting signal is used to filtercaptured images that fall outside of an acceptable pressure range. Forexample, image capture can be limited to only capture while the pressureis within the acceptable range or captured image data can be stored andscreened thereafter. In another example, the captured image data cananalyzed by weighting or otherwise adjusting the IMF calculationsaccording to the resulting signal (e.g., by accounting for the pressurein the calculations or reducing the significance of data from imagesassociated with certain pressure levels).

The above overview is not intended to describe each illustratedembodiment or every implementation of the present invention. The figuresand detailed description that follow and in the appended claims, moreparticularly exemplify these embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be more completely understood in consideration of thedetailed description of various embodiments of the invention thatfollows in connection with the accompanying drawings in which:

FIG. 1A shows a system-level diagram, consistent with an exampleembodiment of the present invention;

FIG. 1B shows a flow diagram for determining IMF content, consistentwith an example embodiment of the present invention;

FIG. 2 illustrates ultrasonic scanning of a hot carcass within a packingplant environment, consistent with an example embodiment of the presentinvention;

FIG. 3A shows an exemplary hardware subsystem, consistent with anexample embodiment of the present invention;

FIG. 3B shows a transducer fixture with pressure sensors, consistentwith an example embodiment of the present invention;

FIG. 3C shows various input and output interfaces, consistent with anexample embodiment of the present invention;

FIG. 3D shows various components of an exemplary power distribution unit324, consistent with an example embodiment of the present invention;

FIG. 4A illustrates the change in the voltage and voltage change ratefor top and bottom pressure sensors within a carcass video frame,consistent with an example embodiment of the present invention;

FIG. 4B illustrates the predicted IMF and the range of acceptablepredicted IMF within a carcass video frame, consistent with an exampleembodiment of the present invention;

FIG. 4C illustrates the dependence of predicted IMF on the rate ofchange of voltages for top and bottom pressure sensors; consistent withan example embodiment of the present invention;

FIG. 5 presents an overall flow chart of an image acquisition procedure,consistent with an example embodiment of the present invention;

FIG. 6 illustrates an analysis procedure for image sequence, consistentwith an example embodiment of the present invention;

FIG. 7A illustrates the types of algorithms and overall flow chart ofimage frame processing steps used for an experimental study, consistentwith an example embodiment of the present invention;

FIG. 7B illustrates fat thickness and muscle depth determinations, alongwith ROI selection, consistent with an example embodiment of the presentinvention;

FIG. 8A indicates one level of wavelet decomposition in three steps oflow and high pass filtering, consistent with an example embodiment ofthe present invention;

FIG. 8B shows a three level pyramidal structured wavelet decompositionof image data, consistent with an example embodiment of the presentinvention;

FIG. 9 is an ultrasound image of a swine carcass loin eye muscle,consistent with an example embodiment of the present invention; and

FIG. 10A shows a block diagram of an example fat depth automationalgorithm, consistent with an example embodiment of the presentinvention.

FIG. 10B shows a block diagram of an example loin depth automationalgorithm, consistent with an example embodiment of the presentinvention.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

The present invention is believed to be useful for inspecting andmeasuring muscle tissue parameters, such as fat and lean composition andquality of muscle tissue. The muscle tissue can originate from anynumber of different food animals and the inspection and measuring can beobtained from live swine or pork carcasses. A specific embodiment of thepresent invention facilitates measurement of intramuscular fat (IMF) ofa pork carcass. Unless otherwise stated, the term “animal” refers toeither a live animal or an animal carcass. While the present inventionis not necessarily limited to such applications, various aspects of theinvention may be appreciated through a discussion of various examplesusing this context.

An embodiment of the present invention is directed toward a noninvasivemechanism for determining IMF content of muscle tissue, such as muscletissue from live pork animals or pork carcasses. Ultrasound imaging isused to capture internal images of the muscle tissue. An image processorprocesses the images using algorithms specifically selected and/ortailored to use with the particular muscle tissue (e.g., the type offood animal or whether live or dead) to determine the IMF content.

Specific embodiments of the present invention are directed towardfacilitating the determination of pork loin IMF content in apork-carcass processing line (e.g., in a muscle tissue packing plant).Devices, methods and systems facilitate IMF content determinations atspeeds and accuracy levels that are particularly useful for use on aprocessing line. Various aspects include, for example, streaming imagecapture, image selection criterion, specifically tailored algorithmsand/or facilitating proper contact between the carcasses and a probe.

One other aspect of the present invention involves the use speciallydesigned algorithms with carefully selected parameters. These algorithmsand their parameters provided surprisingly accurate results. Use ofthese algorithms and parameters, discussed in more detail below, haveresulted in surprisingly accurate and efficient tissue characterization.Moreover, embodiments of the present invention are built upon therealization that filtering of the image data or filtering of parametersfrom the image data can be particularly useful for real-timeapplications that demand highly accurate characterizations of muscletissue.

Embodiments of the present invention provide IMF content measurementsusing ultrasound imaging. These measurements are surprisingly consistentwith even objective chemical-based measurements of IMF content.

Embodiments of the present invention facilitate proper placement of anultrasound transducer on the skin of the carcasses. The inventors haverecognized and appreciated that accurate IMF measurements can beobtained by ensuring that images used in determining the IMF content aretaken with the proper pressure between the ultrasound transducer and thecarcass skin. In a specific embodiment, one or more pressure sensors areused to provide feedback regarding the pressure between the ultrasoundtransducer and the pork carcass skin.

Embodiments of the present invention are directed toward the use ofimage processing to determine the IMF content of pork carcasses.Quantitative parameters of ultrasound/tissue interaction used for suchimage processing include, but are not limited to, signal strength,distribution, and scattering. Aspects of the present invention aredirected to facilitating the derivation of such quantitative parameters.Image processing methods such as texture analysis indirectly provideinformation about the tissue scattering. IMF deposits cause theultrasonic waves to scatter. Constructive and destructive interferenceof waves from such scattering produces graininess, or a texturalpattern, in ultrasound images. These patterns are affected by propertiesof the IMF deposits such as size, density, and distribution.

The present inventors have recognized and appreciated that the distincttexture pattern produced in ultrasound images based on the content anddistribution of IMF may be used to objectively and non-invasivelyestimate IMF automatically, in real time, and at line speed.

An embodiment of the present invention is directed toward a noninvasivesystem for measuring the percentage IMF along with subcutaneous fatdepth and muscle depth in the longissimus dorsi muscle of hot carcasses.The measurements are made real-time on carcasses that are moving on atransport rail at a nearly constant rate of 1,200 carcasses per hour.Measurements are made from live video-streaming ultrasound images as thecarcasses move past a scanning station. The scanning station can befully automated, manual or a combination thereof.

System output data is interfaced with the packing plant individualcarcass identification system and hot carcass weighing scale. Thecombined data is used by the plant to determine individual carcass valueand can be useful for differentiating and sorting of each carcass foralternative fabrication and processing of wholesale pork products withinminutes after harvest.

Embodiments of the present invention are also suitable for determiningIMF or other muscle tissue characteristics of live animals such as liveswine. Because IMF and other muscle tissue characteristics are at leastin part dependent on genetics, measuring live or recently harvestedanimals may produce data to facilitate breeding processes, feeding orcare regimens, and so forth aimed at achieving a higher yield oflivestock exhibiting desired muscle tissue characteristics.

By way of example, FIG. 1 illustrates a system for use in inspecting andmeasuring muscle tissue parameters in carcasses, according to anembodiment of the present invention. Probe 102 communicatively connectsto processing block 104 using probe input/output (/O) 110, 112. Thisconnection can be implemented using, for example, a wired connection,wireless connections or a removable storage medium. Wired connectionscan be implemented using any suitable (e.g., bandwidth and reliability)protocol including, but not limited to, universal serial bus (USB), IEEE1394 and Ethernet. In a specific instance, the probe is connected usinga data-carrying cable (e.g., electrical or optical). In anotherinstance, the probe is integrated into a single device that includes theprocessing block 104. Wired connections can also be implemented using amore temporary connection, such as a removable data storage device or acradle for placement of the probe. Wireless connections fornon-ultrasound communications can be implemented using an assortment ofdifferent techniques and protocols including, but not limited to,802.11x or ultra-wideband (UMB).

Probe 102 provides images of the carcass using ultrasound imaging. Anultrasound transducer 106 converts control data into transmitted soundand received sound into image data. In a specific example, thetransducer is a piezoelectric transducer that converts betweenelectrical and physical vibration energy. Embodiments of the inventionare designed to allow use of a variety of existing or future imagingtechniques (e.g., other than piezoelectric transducers). The actuationof such transducers can be controlled by ultrasound controller 130. Forexample, controller 130 can provide a stimulus profile for capturing aseries of images from the same carcass.

Embodiments of the invention include a human-machine interface (HMI)108. HMI 108 facilitates operation, monitoring or otherwise interfacingwith the system by a human operator.

Image selection block 114 is an optional component that selects betweena set of images obtained from the probe 102. Image selection block mayfacilitate the selection of images based on direct or secondary indiciaof image quality or usability. For example, acquired images may bescreened for blurriness, the existence or absence of certain features,the existence or absence of one or more subset regions of interest (ROI)within the image, and for conditions under which the images wereacquired.

With respect to image acquisition conditions, it has been observed thatthe quality and repeatability of ultrasonic images acquired from animalcarcasses can be affected by the pressure applied between the probetransducer and the carcass skin. Thus, in reference to FIG. 1A, theprobe 102 optionally includes one or more pressure sensors such as loadcells 116A and 116B. Information from the pressure sensors may be usedby an image filter 118 within the probe 102 to decide whether to captureand transmit images to the processing block 104. In other embodiments,the pressure data is transmitted to the processing block 104 foranalysis, at which point the images may be recorded using video capture128 and/or buffer 122 and retained for further analysis or discardedbased on the pressure readings. In another example, the processing block104 analyzes the pressure data and in response determines whether or notto activate the ultrasound transducer. Feedback signals may be providedto control further image acquisition by the probe and/or to provide anoperation status indication (e.g., yellow light for non-acquisitionstand-by mode when the probe is not applied or insufficient pressure isapplied, red light for non-acquisition based on too much pressure orunbalanced pressure, and green light for ultrasonic activation and imageacquisition due to proper application of the probe).

During image selection screening, images are removed or discarded if thequality and/or pressure criteria are not met. In certain embodiments,images discarded based on such screening may be stored for lateranalysis, for example, to facilitate system diagnostics, for adjustingof screening algorithm parameters, and so forth.

According to specific embodiments of the present invention, theprocessing parameters 120 used by the algorithms for determining IMFpercentage estimates can be dynamically adjusted for each carcass. Forexample, each carcass has a specific makeup with regards to the tissuedepths of various tissue types. These differences can affect thecaptured image data as, for example, different tissue types can exhibitdifferent sound propagation properties. Tissue types that can bemonitored for dynamic adjustments include, but are not limited to,subcutaneous fat, muscle (loin), skin and bones. In a specific instance,the subcutaneous fat depth and loin depth within a region of interestare determined. These determined depths are then used as parameters inthe algorithms for the specific carcass.

The IMF percentage determination 124 can be provided for immediateviewing using HMI 108 and/or recorded 126 for future use (e.g., sorting,recording, pricing and feedback for genetic profiling).

Aspects of the invention are directed to calibration of the device. Thecalibration can be particularly useful for maintaining consistencybetween measurements where, for example, components of the device arereplaced or operation parameters change (e.g., changes over time due touse or due to temperature variations). One mechanism for calibrationinvolves the use of a default device that is already calibrated.Measurements are taken for each device and the parameters for the deviceunder calibration are modified so that the IMF readings coincide withthe IMF readings of the default device. Another mechanism involves theuse of a known item from which IMF readings are taken. The item could beone or more carcasses. The measured IMF readings for the device undercalibration can be compared to the actual IMF values of the carcasses.Alternatively, the item could be a specially constructed test apparatus.The apparatus can be constructed to test the response parameters of thedevice under calibration (e.g., using materials having a variety ofdensities and thicknesses and/or simulating a carcass). The readingsfrom the device under calibration can be used to define the particularresponse characteristics and to modify the algorithms accordingly.Another aspect of calibration can include stimulus profiles that definehow the probe is activated during the calibration process.

Aspects of the present invention relate to selection of a region ofinterest (ROI) within image(s) captured by the probe. Such a selectionof an ROI can be particularly useful for reducing the computationalpower needed to process the image(s) and/or for improving the accuracyof IMF calculations by excluding less-than-ideal portions of the image.

FIG. 1B shows a flow diagram for providing an IMF content estimation,according to an example embodiment of the present invention. The systemstores a set of images obtained from a carcass 132. These images can beassociated with pressure readings 134. The association can be anexplicit data bit stored with the images (e.g., database association ortag added to the images) or can be implicit due to filtering of theimages prior to storage (e.g., the act of storing implies that theimages fall within the desired pressure range). Image processing 138involves use of the set of images 132 to calculate the IMF percentages.One component of image processing 138 involves parameter data 142. Othercomponents can include, for example, pressure-based selection orparameter adjustment 136 and/or image quality-based selection orparameter adjustment 140. Each of these components 136 and 140 can beused to exclude various images, such as those that do not meet pressureor image quality criterion. Alternatively, (or in addition to such imageexclusion), components 136 and 140 can be used to modify the parameters142 for respective images. In one instance, this modification can takethe form of reduction in the statistical contribution of images withless-than-ideal pressure readings or having low-quality of image (e.g.,blurred images or images with poor contact). In another instance, themodification can include compensations to the parameter data 142. Forexample, images associated with certain low pressure readings may resultin incorrect IMF content determinations. Where such incorrect IMFcontent determinations deviate from the actual IMF content by apredictable amount, the determinations can be adjusted accordingly.

By way of example, FIG. 2 illustrates a packing plant environment wherehot carcasses, such as carcass 294, are conveyed along an overheadconveyor system 292 in a direction indicated by the arrow. As thecarcasses pass an operator measurement position, an operator 290 appliesan ultrasonic transducer probe from ultrasound system 218 to a specifiedportion of the carcass 294. Images acquired from the ultrasound system218 are provided via connection 219 to a data acquisition system fordata analysis.

Systems in accordance with certain embodiments include a hardwaresubsystem that includes I/O components for obtaining data from a carcassand for interfacing with an operator. A data processing subsystemincludes components for screening and processing data and for providingresults for output. While a number of different implementation optionsexist (e.g., the data processing subsystem may be suitably implementedusing hardware circuitry, programmable logic devices, firmware,software, and combinations thereof), the following description providesa specific implementation as an exemplary system.

The hardware subsystem includes components for use in the capture ofultrasound data in the form of video images, for example, for onlineprocessing of animal carcasses. The hardware subsystem may beimplemented as a highly-portable, rugged equipment enclosure withincorporated electrical and electronic components. An exemplary hardwaresubsystem 310 is illustrated in FIG. 3A, with various component systemsshown in additional detail in FIGS. 3B, 3C and 3D. Hardware subsystem310 includes a host computer 312, an ultrasound system 318 coupled totransducer fixtures 320, an operator interface 314, a video system 316,a data acquisition system 322, and a power distribution unit 324. Thehost computer 312 may provide a platform on which to run the softwaresubsystem.

An operator interacts with the system through the human-machineinterface 314. Power distribution unit (PDU) 324 distributes electricpower, for example from utility power mains, to various sub units. Inaddition to providing a switch for normal power up and power down, thePDU may prevent poor-quality electrical power from reaching thesensitive electronic components within the hardware subsystem 310.

Data acquisition system 322 gathers information from force sensorsincorporated into the ultrasound transducers to determine properpositioning and pressure of the ultrasound probe on the carcass skin.Feedback signals may be generated for the operator to view, for example,using light-emitting diodes (LEDs) to indicate a probe status, such asthe ultrasound probe is ready to scan the carcass, the ultrasonic probeis properly positioned (e.g., the correct amount of pressure), theultrasonic probe is improperly positioned (e.g., the incorrect amount ofpressure), data acquisition is in progress, data acquisition has ended,and so forth.

In certain embodiments, ultrasound system 318 includes a portableveterinary ultrasound scanner console and a hand-held ultrasound probe(or transducer). The ultrasound system 318 creates an image from soundby producing a sound wave, receiving echoes, and interpreting thoseechoes. In a specific example, the ultrasound probe uses a piezoelectricportion that resonates at the desired frequency by electrical pulsesfrom the ultrasound scanner console. Sound waves are received again bythe transducer after interacting with the carcass sample. The consoleassembles video images from these echoes. Video images from the consoleare sent to the video system 316 to be routed and digitized before beingsent to the host computer 312 in the form of individual video frames.

In certain embodiments, video system 316 includes a frame grabber andvideo input/output (I/O) connection hardware. The frame grabber capturesindividual video frames so that digitized images may be sent to the hostcomputer 312, for example, via USB 2.0 high-speed interface. Video I/Oconnections may be made, for example, with 75Ω professional qualityRG-59U coaxial cable and 75Ω BNC connectors and jacks.

Human machine interface (HMI) 314 allows an operator to interact withthe hardware subsystem 310. In reference to FIG. 3C, operatorinteractions may proceed by way of a touch-screen display 344 (or othersuitable input device or devices) and an emergency stop switch 342.Results generated in accordance with the present invention may beoutputted to a head mounted display (HMD) 348, system status LED display346, and the touch-screen display 344. The operator interfaces with thesystem using the touch-screen display 344, for example, a sealedresistive touch-screen display. System status LED display 346 may besealed and located on a bank next to the touch-screen to indicate properfunctioning of the major components and act as a centrally-locatedstatus check. For example, the LEDs may be lit green for a functionalcomponent and blink red when the component has failed or not performingcorrectly.

HMD 348 is an output peripheral that places a micro display in front ofthe operator's eye in order for them to view data from the system. Thedata can include, for example, carcass images generated by the system.HMD 348 may be a rugged see-through prismatic display mounted on thevisor of a hardhat or on safety goggles.

Transducer fixture 320 includes components for obtaining ultrasoundimages from the carcass sample. In one embodiment, the ultrasonic probeincludes one or more pressure sensors located close to the transducerface, for example, one pressure sensor near the top of the transducerface and one pressure sensor near the bottom or an array of pressuresensors dispersed about the transducer face. The pressure sensor(s) areresponsive to the transducer face contacting the carcass and provide asignal to the system that is used to record the pressure applied betweenthe transducer face and the carcass. The recorded pressure data isassociated with the images taken while at the respective pressuresignal, and may be used for image screening (e.g., only imagesassociated with pressure readings greater than a threshold value orfalling within a specified range are analyzed) and/or for correction ofoutput value (e.g., pressure data may be correlated to correction valuesthat can be applied to the system results prior to outputting a finalvalue). Pressure data may also be used to facilitate proper applicationof the transducer fixture to the carcass. For example, LEDs may beincorporated into the transducer fixture to implement a three-colorlight scheme whereby yellow indicates a standby status where thetransducer is not on the carcass, green indicates that the transducer ison the carcass and the applied pressure is at a predetermined level orwithin a predetermined range (e.g., controlled or specified insoftware), and red indicates a fault situation such as when the appliedpressure is too high for proper data acquisition, an insufficient numberof valid data frames were acquired during a data acquisition time frame,and so forth.

Referring to FIG. 3B, load cells 352 and 354 of transducer fixture 320sense the force applied between the ultrasound probe and the carcassskin. The load cells convert the force acting on it into a measurableelectrical signal. Changes in the load result in a proportional changein the signal. Any suitable load cell may be used including, but notlimited to, displacement sensors that sense variable capacitance betweenelectrodes with respect to movement of the electrodes in response to anapplied force.

In-line amplifiers 356 and 358 boost the strength of the load cellsignals to a level usable by the data acquisition system. The amplifiersmay be adjustable for each load cell, for example, to respond byoutputting 0.5 Volts for every 1 pound of force applied.

Force data is sent through the data acquisition system to the capturingsoftware and recorded with the images. Software may be used to decide ifthe ultrasonic probe transducer face is properly positioned and toprovide feedback status information, for example, visually through probeforce status LEDs 360.

In one embodiment of the present invention, digital display 362 is awashable, multi-digit LED readout mounted on the fixture, and may beused by the operator to associate a unique carcass identificationnumber/name with a particular carcass. The current number of the sampleis displayed until a new carcass is in position to be scanned. Automaticidentification and data capture (AIDC) methods may be used, such asbarcodes or RFID tags, to get data about samples and to enter that dataautomatically. A barcode may be imprinted on the sample so that the I.D.is machine-readable by an optical scanner. Passive radio-frequencyidentification (RFID) tags can be applied to or incorporated into ananimal carcass for the purpose of identification using radio waves.

Working in conjunction with the carcass identification number digitaldisplay is an I.D. number increment switch 364 also mounted on thefixture 320. The switch 364 may be implemented as a button that theoperator can press to manually advance I.D. The switch may be of thetactile feel feedback type that responds with a mechanical signal(typically a “snap” or “click”) to indicate contact closure to theoperator.

For purposes of scanning carcasses at a rapid rate by a human operatoror by a robotic arm, the ultrasonic transducer fixture assembly may besupported by an overhead counter-balance and attached cable, andoptionally provided with guides so the operator can quickly and properlyalign the transducer on the carcass.

Measurement of carcasses online in packing plants can be performed byhuman operators with the aid of measuring devices. However, humans tireand become distracted when doing monotonous activities. A robotic systemoffers repeatability and precision in application of the measuringdevice, even on moving carcasses. For example, a robotic system mayemploy a six-degrees-of-freedom arm guided by laser-vision sensors thatscan each carcass to determine the precise positioning of the transducerand its fixture on the carcass. Variation in size and shape ofindividual carcasses can be accounted for so that linear measurement ofsubcutaneous fat and muscle measurements are made at the same relativeanatomical position on every carcass. Various alternative locationtechniques can also be employed. For example, a human could mark atarget location on the carcass (e.g., placing a visible mark on thecarcass at the appropriate location). The automated arm can search forthe marked location and properly place the ultrasound sensor based uponthe marked location. This can be particularly useful for reducing thecomplexity of the positioning determination (e.g., simplifying automatedmachine vision and image processing) while allowing the actual placementand contacting of the ultrasound sensor to remain automated. Proximityand pressure sensors are used to insure that the transducer face isproperly applied to the carcass for the capturing of images required forloin muscle tissue characterization for the prediction of percentageintramuscular fat.

In reference to FIG. 3D, various components of an exemplary PDU 324 areshown. In certain embodiments, PDU 324 powers all components by 120Volts AC, 60 Hz, single phase, through a 2-pole, 3-wire rubber powercord 328 and a grounded straight blade or locking NEMA L5-15Pwater-tight plug that connects to available power supply mains. Sitewiring fault indicator 330 is an electrical unit that is illuminated ifthe power cord 328 is plugged into an improperly wired utility poweroutlet. Wiring faults detected include missing ground, hot-neutralpolarity reversal, and overloaded neutral circuit. Surge/transientsuppressor 332 protects equipment from over-voltages present on the ACpower. The suppressor 332 operates by absorbing or blocking a surge fromdamaging the sensitive hardware components. Line conditioner/EMI filter334 improves the “quality” of the power that is delivered to electricalload equipment by helping prevent voltage sags and surges from reachingthe equipment, filtering small utility line fluctuations, and isolatingequipment from large disturbances. The Electro-magnetic emissions EMIfilter portion of PDU 324 attenuates conducted radio frequencies (RFI)between the line and the equipment. Master power switch and circuitbreaker 336 may be implemented as a single sealed power switch on themain face of the PDU enclosure to perform normal power up and power downof the hardware subsystem. Fail-safe latch circuit 338 is used to latchall power circuits in a fail-safe manner. This circuit is a securitymeasure used to shut off the hardware subsystem in an emergencysituation in which it cannot be shut down in the usual manner, and maybe operated in conjunction with an emergency-stop switch on the mainface of the PDU enclosure. Output receptacles 340 are socket-type femaleelectrical connectors that have slots or holes which accept the pins orblades of power plugs inserted into them and deliver electricity to theplugs. The receptacles may be conveniently located on a rear panel ofthe PDU, and may be hospital grade rated, such as 125 VAC 20 A NEMA5-20R.

The electrical systems may be housed in a water-tight,corrosion-resistant enclosure suitable for sanitary wash-downenvironments. For example, the material of the enclosure structure andbody may include 300 series stainless or better. The top of the unit maybe sloped to reduce standing and pooling of water and cleaningsolutions. The hardware unit may be mounted on resilient casters withlocking mechanisms for ease of portability and stability when in use.

Accessibility for maintenance and routine service is available throughaccess panels which are sealed with a non-porous continuous gasketcapable of withstanding high pressure, high temperature jet streamwash-down. Heat Dissipation is managed in the unit by static ventilation(radiation/convection), whereby excess heat is transferred to theenclosure structure and body.

A specific embodiment of the present invention includes a pressuresensing fixture that mounts to the ultrasonic transducer and that can bedisassembled for cleaning or repair as needed. In certain embodiments,the pressure sensing fixtures include two pressure load cells locatedand operated perpendicular to the face of the ultrasonic transducer(i.e., parallel to the direction of ultrasound wave propagation). In anexemplary arrangement, one load cell is located near the top end of thetransducer (e.g., within 1 cm of the top) or near one end of thetransducer and the second load cell is located near the bottom end ofthe transducer (e.g., within 1 cm of the bottom) or near the oppositeend of the transducer. In another exemplary arrangement, the load cellsare embedded into and are a part of the transducer lens and are inintegral part of the transducer probe. These load cells indirectlymeasure the pressure being applied between the transducer lens face andthe carcass as the transducer is applied to the carcass skin surface bythe human operator or by a robotic arm.

Software algorithms or hardware are used to monitor the pressurereadings from each load cell. The system associates the pressurereadings with the video frames that were captured at the same time thatthe readings were acquired. In certain embodiments, live video streamingframes are used to calculate IMF content only when the load cellreadings indicate that the transducer is being applied to the skin ofthe carcass within a specified range of pressure, for example, pressurehigher than a minimum threshold, pressure lower than a maximumthreshold, and/or pressure difference between the load cells is lessthan a maximum difference. The software may be used to controlindicators such as two LEDs, one for each load cell. The processingsoftware sends a code for turning the LED yellow if the pressure forthat particular load cell has not reached a minimum level for acquiringimages that allow proper tissue texture analysis. The processingsoftware sends a code for turning the LED green when an appropriatepressure or pressure range is achieved. The processing software sends acode for turning the LED red if the pressure exceeds an acceptablepressure level. The frames captured outside the allowable pressure rangemay be rejected as not suitable for processing, although they may besaved for later analysis and system diagnostics purposes. Pressure levelparameters within the processing software may be adjustable by servicetechnicians and allow for maintaining proper calibration of the fixtureand sensor configuration.

FIGS. 4A, 4B and 4C illustrate the dependence of predicted IMF on thepressure exerted by the transducer on the skin of the carcass. In FIG.4A, the plot shows the voltage level being sent from the top pressuresensor and the bottom pressure sensor. The voltage level increases aspressure increases, e.g., as the transducer is being applied to the skinsurface. The voltage level for each sensor increases to some steadystate value as the operator seeks to stabilize the quality of the videostream of frames, with only minor change in value being seen. FIG. 4Aalso shows the rate of change of each sensor's voltage level. When thetransducer is being applied to the skin of the carcass, and then againwhen the transducer is being removed, the voltage rates of change are attheir peak values. An unexpected result is that both the voltage leveland the rate of change of voltage are important parameters for thepressure filter. To provide high-levels of the accuracy in the tissuecharacterization, it can be important to verify that the image frameswere captured with both the correct threshold range of pressure and astabilized the rate of change that is also within a maximum thresholdrange. For example, if the current or previous frame sensor voltage isless than or equal to 0.49, then the current frame is ignored.Concurrently, if the difference between the current frame voltage andthe previous frame voltage divided by 0.067 (the voltage rate change) isgreater than 1.3, the current frame is ignored.

FIG. 4B shows the surprisingly accurate predicted IMF as it relates tothe series of image frames corresponding to the pressure sensor valuesof FIG. 4A. As shown, the acceptable period for predicting IMF issurprisingly correlated with the parameters from the pressure sensor.FIG. 4C shows the overlay of the data from FIGS. 4A and 4B, furthershowing the surprising correlation between the pressure sensor input andthe predictive accuracy in IMF measurement.

The hardware subsystem interfaces with and is controlled by the softwaresystem, which also screens and processes the captured ultrasound image.Each frame, or snap-shot, of the acquired ultrasound video signal isprocessed using selected sets or subsets of image processing algorithmsto derive attributes of interest, such as IMF. Multiple such singleframes are captured for each carcass to be evaluated.

Ultrasound video capturing and processing at a faster frame rate may beused advantageously for automated processing as well as certainapplications such as chute-side analysis and carcass evaluation at afaster line speed (e.g., 1200 carcasses per hour). In accordance withcertain embodiments, systems and methods of the present invention areused to capture and process ultrasound video imagery at variable framerates (e.g., from 1 frame per second (fps) to 60 fps). Variousprocessing modules or sets of processing modules can be selected andapplied to the series of captured frames to extract tissuecharacteristics and attributes. Possible processing algorithms includeframe editing, automatic image quality detection, fat thickness andmuscle depth evaluation, and image texture processing.

In exemplary embodiments, the present invention may be used to firstscreen the acquired images for sufficient image quality. Next, imageprocessing algorithms may be applied to automatically determine the fatlayer boundaries, and then determine the rib locations (if visible onthe image) and the top and bottom edge of the intercostales muscles. Inaccordance with certain embodiments, the present invention thendetermines one or more ROI of an image frame for further analysis, andselects and applies one or more image processing techniques in sequenceor in parallel to the determined ROI. Automatic image processing and ROIdetermination can be used to reduce errors due to subjectivity of humanintervention in interpretation of images. The further analyses are basedon developed parameter values that may be used to generate an outputvalue for a desired characteristic, such as IMF. Each of these steps, aswell as the determination of parameter values for exemplary embodiments,is described in more detail below.

Video frames are continuously captured and processing of the capturedimages is implemented in response to the sensors on the transducerfixture indicating that a correct carcass skin to transducer lens facepressure range has been achieved. The pressure can be continuouslymonitored. Each frame for which a corresponding pressure measurementmeets the pressure range criteria is evaluated for ultrasoundpenetration level through the first designated amount of skin (e.g.,0.69 mm for pork) as determined by histogram thresholding along thelength of probe lens. Segments of the frame at the designated depth thatexceed a set reflection intensity level (e.g., 179 pixel grey scale) aregated, and regions below these segments can be excluded from thedevelopment of texture parameters. Segments of the frame at thedesignated depth that exceed a set reflection intensity level (e.g., 200pixel grey scale) are gated, and any region below these segments can beexcluded from a determination of subcutaneous fat depth and muscledepth. Blurred frames as detected by a wavelet transformation algorithmmay be excluded from further processing of tissue texture, but may beused for subcutaneous fat depth and muscle depth.

Frame editing procedures may optionally include frequent monitoring ofthe image contrast level by processing the grey scale bar on theultrasound image, and monitoring for significant electromagneticinterference corrupting image frames.

A threshold step in image analysis is to screen acquired images so thatacceptable images are used in the statistical analysis to develop an IMFregression model. For example, blurred images, images captured oncarcasses where the structure of the skin has been significantly alteredand will not allow the ultrasound to penetrate, and images capturedduring the ingress (placement) and egress (removal) of the probe to theskin surface may be discarded during the screening process. Blurredimages may be detected by a wavelet blurring algorithm so that imageswith a blur factor greater than a specified level (e.g., 0.90 andgreater) are defined as unacceptable and are not analyzed.Alternatively, the blur factor can be used to weight the importance ofthe images by, for example, decreasing the statistical importance ofimages as the blur factor increases. Images captured during placement ofthe probe, determined by the probe pressure sensors having not achieveda threshold of pressure value, can also be defined as unacceptable.Similarly, images captured during removal of the probe and associatedwith low probe pressure values may be discarded. Moreover, image framesthat are captured when either (any) of the probe pressure sensors exceeda threshold amount, or when the difference between any two probepressure sensors exceeds a threshold amount may be defined asunacceptable.

Other image screening techniques include evaluating the images forsuitable portions to determine whether any suitable ROI exists in animage. For example, image regions where the average pixel value over thesame horizontal line position exceeds a predetermined threshold greyscale (e.g., 150) may be flagged as unacceptable. After screening theimages for regions of acceptability and unacceptability, those imagesthat exhibit one or more independent ROI boxes of an appropriate size tobe placed in the image are defined as acceptable; the others are definedas unacceptable. According to a specific embodiment of the presentinvention, the fat depth and loin depth of muscle tissue is determined.Fat depth and loin depth measurements are used in estimating fat freelean content in live and carcass food animals. Fat and loin depthmeasurements using ultrasound images offer a non-invasive method forthis procedure. Automation of these depth measurements from ultrasoundimages can provide fast, efficient and consistent measurements comparedto visual and manual tracing. Automation of depth measurements includesthe automatic determination of the boundary positions for fat top, fatbottom, rib top, and the top and bottom interfaces of the intercostalesmuscles. These interfaces can be measured between all the rib pairs inlongitudinal ultrasound images of live animals or carcass data (e.g., inswine, positioned between the 10^(th) and 13^(th) ribs). This offers theuser the flexibility to select the preferred location for depthmeasurements. The following relationships can be defined:

Fat depth=Fat Bottom boundary−Fat top boundary

Loin depth=Rib top boundary−Fat bottom boundary, or

Loin depth=Intercostales muscles boundary−Fat bottom boundary.

The automation algorithm includes three subsections, each determiningone of the above-mentioned boundary positions. Ultrasound image size(number of pixels along rows and columns) can vary depending onultrasound scanner and frame grabber used for image capturing, and sothe algorithm may be independent of image pixel size. The fat depth andmuscle depth estimates are adjusted for the differences in ultrasoundvelocity in fat and muscle, respectively. A more detailed discussion ofthis procedure is given near the end of this document.

According to an embodiment, analysis of ultrasound images for tissuecharacterization purpose is facilitated by defining one or more “regionsof interest” (ROI) within each image frame to be analyzed. The ROIselection process provides one or more representative areas of the fullimage for evaluation, and helps provide consistency among therepresentative areas evaluated from carcass to carcass.

After image screening, the selected image ROIs may be analyzed. ROIselection procedures are discussed in more detail later. An overview ofvarious image analysis algorithms are discussed below.

Ultrasound images display a cross-section (in the plane of ultrasoundbeam) of a tissue being scanned. The image displays variations in tissuedensity and acoustic impedance. Thus, various boundaries betweendifferent tissues (e.g., fat and muscle) are displayed. Additionally,each tissue has its own characteristic “ultrasound signature” that isdisplayed in the form of texture or speckle pattern on the image. Thistexture pattern also depends on the ultrasound transducer and scannerdesign and processing or the raw ultrasound data. In certainembodiments, the present invention utilizes image processing algorithmsthat can be used to determine the tissue characteristics in porkcarcasses. The types of algorithms and overall flow chart of image frameprocessing are illustrated in FIGS. 5 through 7. For example, imageprocessing based on two-dimensional Fourier transformations providesparameters highly correlated with the actual (chemical) IMF values. Suchparameters may be combined in a prediction formula to estimate IMF incarcasses at a line speed.

Ultrasound calibration software algorithms may be used to set imagecapturing parameters to a given reference. Calibration works incombination with an ultrasound scanning device, the analog video signalfrom the scanner, and an image frame grabber. Calibration software maybe used to automatically determine if the source of the video comes fromany of five different ultrasound equipment types. Based on analysis ofgrey scale bars present in the images from these machines, calibrationestimates actual signal voltage level and compares with a 1 voltreference. Understanding the signal strength differences between scannerbrands as well as between scanners of the same brand may be usedadvantageously in the development of algorithms that can be used topredict % IMF from textural knowledge gleaned from ultrasound images fora variety of ultrasound scanner types.

Calibration also allows selection of the ROI within any given image tocompare contrast histogram properties with a predetermined referenceimage (e.g., with the same ROI selected). The contrast and brightnessdifferences are determined within each line of the image ROI anddisplayed visually, and overall percent differences are quantified andpresented in the analysis window.

Calibration is also used for ultrasound scanner image normalizationalgorithm between different equipment types for texture parameters thatrelate to food animal and carcass tissue characteristics.

FIG. 5 presents an overall flow chart of an image acquisition procedurein accordance with certain embodiments. At step 502 the device canoptionally be calibrated. Calibration can include testing andconfiguration of a number of different elements including, but notlimited to, ultrasound, image capturing and pressure sensors. At step504 the scanning process is setup. This can include scanning of carcassIDs, data storage of necessary information, ROI determinations and thelike. At step 506 the carcass is positioned for scanning. The transduceris positioned (e.g., placed in contact with the carcass) for scanning atstep 508. Step 510 involves the acquisition of a sequence of ultrasoundvideo image frames. These images can be tagged with pressure sensordata, or otherwise filtered according to the pressure data. At step 512,the sequence of ultrasound video image frames are analyzed usingmultiple image processing algorithms. Steps 506-512 can then be repeatedfor subsequent carcasses as desired.

FIG. 6 illustrates an analysis procedure for an image sequence. Theimage sequence acquired from ultrasound video (e.g., capture inreal-time) is first processed for frame editing to discard blank andpoor quality frames. The fat thickness and muscle depth are calculatedby applying image processing techniques (described in more detail below)followed by automatic ROI selection and further texture analysis using aselected set of processing and analysis algorithms depending on theparameters and the tissue characteristic of interest. The textureparameters may be further analyzed by various statistical techniques toselect a desired list of parameters and develop coefficients and rulesfor IMF prediction.

Each image sequence can be identified with a carcass ID. Typicalacquired images processed are of 512×486 pixels or 640×480 pixels, withthe pixel values representing 256 shades of grey, although the describedimage texture analysis algorithms are applicable to any suitable imagesize and pixel range as well as various equipment settings. Specificresults presented in this document are examples of specific equipment,settings and conditions, for example, using commercially availableportable ultrasound scanners and typical equipment settings.

FIG. 7A illustrates the types of algorithms and overall flow chart ofimage frame processing steps used for the experimental study. The imagesequence acquired from real-time ultrasound video is first processed forframe editing to discard blank and poor quality frames. The fatthickness and muscle depth (indicated in FIG. 7B and FIG. 9) arecalculated by applying image processing techniques, which is followed bytexture analysis. Texture parameters may be further analyzed by variousstatistical techniques to select a list of parameters and developcoefficients and rules for IMF prediction.

Image analysis can be performed using computer-based image processingsoftware or dedicated hardware, such as a programmable logic array. Theimage texture processing algorithms may be implemented as librarymodules. The image texture is analyzed by selecting and usingregions-of-interest (ROIs) that are a subset of the overall image (e.g.,100×100 pixels or 80×80 pixels for the image sizes described above). Thesoftware may allow selection of additional ROI sizes, for example, toaccommodate processing of smaller or larger muscle. Image processingthen proceeds using one or more ROIs from each acquired image. Anexample selected image ROI is indicated in FIG. 7B.

Texture parameters are calculated from the ROIs based on the selectionamong several texture-processing algorithms, which include first-orderstatistical analyses such as histogram analysis, second-order statisticsusing co-occurrence matrix analysis, gradient processing, 2D Fouriertransformation analysis, wavelet transformation analysis and fractalanalysis, and so forth. Selection of parameters and texture-processingalgorithms may depend on which parameters are best correlated to thetissue characteristics of interest. The degree of correlation along withcoefficients used in the algorithms may be determined empirically, forexample, by applying the texture-processing algorithms and comparingpredicted results to a measurement of the tissue characteristics ofinterest.

Image ROI can be represented by a first-order probability distributionof image pixel amplitude (grey level). The shape of the image histogramcharacterizes the image. For example, histograms having wide amplitudedistributions may indicate a high-contrast image. Histogram parameterssuch as mean, variance, skewness, kurtosis, mode, and percentiledistribution provide information about image darkness, brightness, andcontrast. In general, such image characteristics will depend on theequipment including its calibration and settings, as well as consistencyof scanning procedures. As such, images characteristics calculated fromfirst-order statistics may be better suited for providing informationabout the overall image quality than for providing parameters forquantifying the texture. In accordance with certain embodiments,acquired images may be screened based on the values of textureparameters from first-order grey level histogram, such parametersincluding skewness of the grey scale histogram (referred to herein asparameter p7), standard deviation of the grey scale histogram (referredto herein as parameter p16), and coefficient of variation of the greyscale histogram (referred to herein as parameter p17).

Image texture assessment may be performed by second-order-statistics,for example, based on co-occurrence matrix analysis. The co-occurrencematrix is a joint probability distribution of pairs of geometricallyrelated image pixels. Co-occurrence parameters provide information ontexture or speckle patterns in an image. This is based on the idea thatthe texture information in an image is contained in the overall spatialrelationship which the grey tones in the image have to one another. Aset of grey-tone spatial-dependence probability-distribution matricesmay be computed from a given image block, and several textural featuresmay be extracted from these matrices. These features contain informationabout image texture characteristics such as homogeneity, grey-tonelinear dependencies, contrast, number and nature of boundaries present,and the complexity of the image. Grey level spatial-dependenceprobability distribution matrices are calculated at angles of 0, 45, 90,and 135 degrees. These matrices are then used to calculate severaltexture parameters such as contrast, sum entropy, difference variance,and correlation, as defined in image processing literature.

Fourier transformation techniques transform data into a form thatprovides information on the occurrence and frequency of repetitivefeatures in an image. Fourier transformed images from a selected ROI maybe used to determine the distribution of power at different frequencies.From such distributions, the rate of change in power from one frequencyto another may be calculated using curve fitting algorithms and ratiosof powers within different frequency ranges.

For a given image, let the ROI be of size N×N, and represented by I(x,y)which is function describing the grey level in x and y spatialcoordinates. The Fourier transform F(u,v) is calculated according to theequation below, where u and v are spatial frequencies and 0<u, v<N−1.

${{F\left( {u,v} \right)} = {\frac{1}{N^{2}}{\sum\limits_{y = 0}^{N - 1}\; {\sum\limits_{x = 0}^{N - 1}\; {{I\left( {x,y} \right)}^{{- j}\; 2\; {{\pi {({{xu} + {yv}})}}/N}}}}}}},$

The Fourier power spectrum may be computed as Fp(u,v)=F(u,v)F*(u,v)=|F(u,v)|², where Fp is the sample power spectrum and * denotesthe complex conjugate. The power spectrum is circularly shifted so thatthe center represents (0,0) frequency.

A coarse image texture shows high values of Fp concentrated near theorigin, while a fine image texture shows a more spread out distributionof values. Similarly, a texture with edges or lines in a given directionθ has high values of Fp concentrated near θ+π/2, while homogeneoustextures have little or no directional concentration of Fp values.

From Fourier power spectrum, two types of features are commonlycalculated using annular and wedge sampling geometries. The ring shapedsamples are calculated as:

${{F_{R}\left( {r_{1},r_{2}} \right)} = {\sum\limits_{\underset{{0 < u},{v < {N - 1}}}{r_{1}^{2} \leq {u^{2} + v^{2}} < r_{2}^{2}}}\; {F_{p}\left( {u,v} \right)}}},$

where r1 and r2 are inner and outer ring radii, respectively. The ringfunction is calculated for every radius of one pixel thickness (i.e.,r2=r1+1). The function value for each ring is normalized by averagingover the number of pixels within the ring.

Typically, ultrasound image texture produces a ring function that can beapproximated using an exponentially decaying function of the formF_(R)(r)=ae^(−br), where r is the ring distance from the center. Thecoefficients a and b are used as descriptors of Fourier powerdistribution. The coefficient b can be considered as a measure of theratio of high spatial frequency to low spatial frequency information.Additionally, the ring function values may be further characterized byratios of power between two specific frequency bands. For example, aratio of sums of ring values for radii less than 50% (normalized radiusof half the width of the ROI Fourier transform) and radii more than 50%is calculated as:

${{{FRP}\; 50} = \frac{\sum\limits_{1 \leq r < {N/2}}\; {F_{R}(r)}}{\sum\limits_{{N/2} \leq r < {N - 1}}\; {F_{R}(r)}}},$

where the Fourier ring value at radius 0 is ignored to avoid strong biasintroduced by very high value at frequency (0,0) representing averagegrey value.

The Fourier wedge sampling geometry is defined as:

${{F_{W}\left( {\theta_{1},\theta_{2}} \right)} = {\sum\limits_{\underset{{0 < u},{v < {N - 1}}}{\theta_{1} \leq {\tan^{- 1}{({v/u})}} < \theta_{2}}}\; {F_{p}\left( {u,v} \right)}}},$

where θ1 and θ2 are the angles that define the wedge originating from(0,0). The Fourier wedge features such as mean and ratios may becalculated for the 15-degree wide wedge segments between zero and180-degree angles.

Examples of Fourier transform-based texture parameters include theFourier intensity coefficient of variation (standard deviation dividedby mean), referred to herein as parameter p1; the ratio of Fourierpowers within normalized frequency range of [0.01, 0.50] and [0.51,1.00], referred to herein as parameter p2; and ratio of Fourier powerswithin normalized freq range of [0.01, 0.30] and [0.31, 1.00], referredto herein as parameter p3; and ratio of Fourier powers within normalizedfrequency range of [0.01, 0.10] and [0.11, 0.15], referred to herein asparameter p4.

Wavelet transformation can be used to analyze an image at different timeand frequency scales. Discrete wavelet frame texture descriptors may beefficiently calculated using filter-bank algorithms along with Haarwavelets with a low-pass filter and a corresponding high-pass filter.

FIGS. 8A and 8B illustrate wavelet decomposition using low and high passfiltering. FIG. 8A indicates one level of wavelet decomposition in threesteps of low and high pass filtering in the horizontal direction andvertical direction, and via subsampling. FIG. 8B shows a three levelpyramidal structured wavelet decomposition of image ROI. Fromthree-level wavelet decomposition, energies in three high-pass sub-bandsfor each of the three levels may be calculated as texture parameters.For three-level decomposition, such methodology provides nine textureparameters, named W1 to W9 as follows: W1, W2, and W3 are the energyparameters in the three high-pass sub-bands for level-1 waveletdecomposition; W4, W5, and W6 are the energy parameters in the threehigh-pass sub-bands for level-2 wavelet decomposition; and W7, W8, andW9 are the energy parameters in the three high-pass sub-bands forlevel-3 wavelet decomposition.

The usefulness of image features or parameters derived therefrom dependson the information content and how sensitive and specific the feature isto the differentiation or characterization problem of interest. Inaccordance with certain embodiments, selecting and using sets or subsetsof texture parameters based on ultrasonic images is used in tissuecharacterization and classification. In exemplary embodiments,statistical methods are used to select a set of parameters that showsignificant correlation with chemical IMF and provide robust predictivecapability. In addition, statistical methods may be used to screen andselect acquired images that are most likely to produce reliable results.

As discussed, ultrasound-based systems in accordance with certainembodiments of the present invention are used for live or carcass animalevaluations utilizing multiple-frame image analysis. Each acquiredultrasound image frame is screened sequentially, and ROI of the imagesare selected and processed using image processing algorithms to deriveattributes of interest. Such ultrasound video capturing and processingmay be performed at rates that allow automated processing as well aschute-side analysis and carcass evaluation in real time, potentiallyallow for faster line speeds (e.g. 1200 carcasses per hour or more).

As described, scanning systems include an ultrasound scanner thatproduces ultrasound video image and pressure sensor reading inputs to aprocessing computer that stores the incoming information in real-timeand at line speeds. As with any multi-component system, the slowest ofcomponent determines the final rate of the system. Certain embodimentsof the present invention may be used to capture sufficient numbers ofultrasonic video images at line speeds, and automatically processes theimages using frame editing, image quality detection, fat thickness andmuscle depth evaluation, and image texture analysis to extract tissuecharacteristics and attributes, such as IMF.

In exemplary embodiments, the present invention may be implemented as anonline pork loin IMF prediction system, for example, usable by a packingplant to sort pork carcasses for processing, product marketing, andpaying pork producers for their harvested pigs. Systems and methods ofthe present invention may be employed on hot pork or beef carcasses(hot, meaning within 45 minutes postmortem), and where IMF (or othertissue characteristic) prediction is desired to be performed real-timeso that the data can be interfaced directly with other carcass data andbefore the carcass leaves the hot carcass processing part in theharvesting plant.

Scanning of carcasses moving on a transport system within a packingplant for purposes of predicting IMF level within an individual carcasspresents conditions that may be addressed using systems and methods inaccordance with certain embodiments of the present invention. Forexample, in typical packing plant processing environments, carcasses aremoving by the scanning station at the rate of approximately 1,200carcasses per hour. In other words, there is less than 4 seconds of timeavailable to accurately apply an ultrasound probe on the skin of thecarcass, capture the imagery, perform the analysis to predict IMF (orother characteristics), interface the data with other carcass data suchas animal identification, remove the probe from the skin of the animaland prepare to repeat the process for the next inline carcass.

In systems and methods of the present invention, an operator (human,automated, or combination) positions the ultrasonic probe on the skin ofthe carcass, and the remaining processes follow automatically, includingthe capture of carcass identification and live video image frames.

In exemplary pork loin processing embodiments, the operator positionsand maintains the ultrasound transducer (probe) fixture so that theprobe is vertically aligned with and parallel to the spin or midline ofthe carcass, between 2 and 7 cm lateral to the midline, and on eitherside of the carcass. In typically packing plant environments, thecarcass is vertically suspended on a trolley system. The top portion ofthe transducer face may be positioned so that the ultrasound image willinclude the last 3 to 4 ribs of the carcass.

The procedure for scanning carcasses involves a steady stream of videoframes being captured and stored for each test carcass. For an exemplaryultrasound scanner and probe such as manufactured by ESAOTE Pie Medicaland available under model number Aquila Vet ultrasound scanner modelnumber 401611 ASP 3.5 Mhz probe, the nominal frame rate is 26 fps. Foran exemplary ultrasound scanner and probe such as manufactured by Alokaand available under model number SSD 500V ultrasound scanner and UST5011 3.5 Mhz probe, the nominal frame rate is 11 fps with the normalfocal depth settings of focal zones 2 and 3 being enabled. As will beappreciated, any suitable scanning settings may be used, taking intoconsideration that direct data comparison between carcasses will be morereadily obtained when the selected equipment settings are kept constant(image texture parameters are influenced by pixel grey scale level,which can vary significantly with different settings). Exemplarysettings for the Aloka SSD 500V include a magnification of X2.0, overallgain of 85, near gain set at −25, far gain set at 2.0, frame correlationset to auto, contrast set to 4, and automatic gain control set to 1.

After various selected image screening techniques are applied, theacceptable images for a given carcass are used in the prediction of IMFlevel. During experimentation, it has been observed that screening suchas described above generally results in at least 2 acceptable images andas many as 27 acceptable images during a 30 frames per second imageacquisition scan, with the average number of acceptable frames percarcass found to be 12.6±4.1 out of a total of 7,668 frames evaluatedfrom 700 carcasses.

As noted above, it may be useful to define an acceptable image as onethat allows a minimum of two non-overlapping acceptable ROIs to beplaced and evaluated. In exemplary embodiments, the ROI are positionedwithin the longissimus dorsi muscle, with the ROI size being selected toincrease the texture area to be processed so long as the ROI does notundesirably include interface echoes, rib tops, subcutaneous fat,intercostales muscles or fat interfaces.

The amount of time varies that a probe is positioned on a carcass tocapture live ultrasound video. Also, the number of acceptable framesvaries from carcass to carcass. It has been observed that the IMFprediction model is most accurate using images captured when the probeis effectively positioned on the carcass (defined by acceptable imagesbeing captured) for about 1 or more seconds.

To develop a tissue evaluation model that produces a predictive valuefor a tissue characteristic of interest from analysis of capturedultrasonic video frames, a statistical analysis can be applied todetermine the confidence level associated with various parameters.Prediction of tissue characteristics from image data, and particularlyultrasound image data to predict percentage IMF, was facilitated by anumber of discoveries related to the specific statistical analysis ofthe image parameters and their ability to accurately model the tissuecharacteristics. Such statistical analysis then facilitates theselection of parameters and processing algorithms. In certainembodiments, the present invention combines descriptive tissuecharacterizing texture parameters into a regression model that ispredictive of percentage IMF, and that is accurate according to a set ofR², root mean square error (RMSE) statistical, bias and correlationstandards. The following description provides an experimental processand related data consistent with an example of performing suchcharacterization.

As an initial step, the acquired images are screened for image qualityso that the texture parameters are calculated for a set of images thatconform to editing standards. A natural logarithmic transformation tothe base e of the dependent variable may be used for food animal IMFprediction models in order for the residual errors to conform to anormal distribution assumption. Regression models are optimized forhardware configurations, environment and being either live animal orcarcass. One possible regression model is of the form:

Predicted %IMF=−0.086614860+mz1*1.297367893−mz3*0.086056279−mz4*1.25833393−mz7*1.871074428,where

mz1=0.5*mp1+0.5*mq1,

mz3=0.5*mp3+0.5*mq3,

mz4=0.5*mp4+0.5*mq4,

mz7=0.5*mp7+0.5*mq7,

and where mpi and mqj are individual texture parameter values from ROIiand ROIj within each carcass image, and where final predicted percentageintramuscular fat is equal to e (irrational constant) raised to thepower of the predicted percentage intramuscular fat if a naturallogarithmic transformation is used.

In certain embodiments, methods of analysis according to the presentinvention assume a linear model with dependent variable, y, as thechemical fat data and independent variables as a list of possibletexture parameters that are determined from within the texture of eachdefined ROI within each frame. The y variable is defined as thepercentage of total chemically extractable fat from a loin muscle tissuesample.

Candidate regression models are identified with PROC STEPWISE (SASInstitute Inc., Cary, N.C., USA) and PROC RSQUARE (SAS Institute Inc.,Cary, N.C., USA) for further study using maximum R² and Mallows' C_(p)statistic. Outlier texture parameters within a carcass are identifiedwith PROC ROBUSTREG (SAS Institute Inc., Cary, N.C., USA) and thosecarcass image frames are eliminated from further consideration. Themodel development may be refined with PROC GLM (SAS Institute Inc.,Cary, N.C., USA). Analysis models where the level of significance foreach independent parameter is <0.0001 may be selected for final modelvalidation. It is useful to consider both first and second orderpolynomial models, and using accuracy statistics such as model R², RMSE,and distribution of residuals. Once a final model is selected, thedependent variable is regressed on the predicted IMF level for allcarcasses within the model development and validation data sets todetermine the prediction model root mean square error (RMSE). The modelis of the general form:

y _(i) =b ₀ +b ₁ *p _(i1) +b ₂ *p _(i2) + . . . +b ₁₀ *p _(i10) +e _(i),

where, y_(i)=% IMF for the i^(th) carcass loin muscle sample, and wherethe final predicted percentage % IMF is equal to y_(i); p_(ij)=j^(th)texture parameter values for the i^(th) carcass (1^(st) and 2^(nd)order); and e_(i)=random residual error for the i^(th) carcass.

The assumption that the residuals, e_(i), are normally distributed isnot necessary for estimation of the regression parameters andpartitioning of the total variation. However, normality may beestablished for tests of significance and construction of confidenceinterval estimates of the parameters. Transformation of the dependentvariable to a form that is more nearly normally distributed is the usualrecourse to non-normality. Heterogeneous variance, as non-normality, maygenerally be expected, and may be handled using transformation of thedependent variable and weighted least squares. It has been observed thata natural logarithmic transformation of the dependent variable, chemicalIMF, is suitable to reduce heterogeneous variance.

It was observed that using two or more ROIs in each analyzed imageimproved model R² approximately 5% over using a single ROI per image.

Development of an IMF prediction model that relies upon the processingof ultrasound imagery includes developing a database of images capturedfrom a large number of individually identified pork carcasses and fromcarcasses that exhibit considerable variation in terms of their IMFlevel. For example, muscle tissue samples can be taken from each loinscanned and from the direct vicinity of the ultrasound scanning andsubjected to a wet laboratory procedure that can be used to objectivelyand quantitatively determine the actual total chemical fat in the tissuesample. Other comparative data that may be collected from each loinscanned includes marbling score, which is a subjective and visuallydetermined numerical score for IMF. Once the database is developed(images, chemical intramuscular fat readings, and marbling score) forthe individual carcasses, a statistical analysis is performed toidentify image textural parameters and their respective and proportionalinfluence on the level of IMF within the loin muscle of an individualcarcass.

At the outset of the statistical analysis, an in depth review of eachframe capture for each carcass is made and a determination is made as tothe quality of the image. Unacceptable images are so classified in thedatabase and excluded from further analysis. A subset of the database isselected for the development of alternative prediction models, and thenpromising candidate models are tested on a different subset for purposesof validation. The final product is a regression model that can be usedfor prediction of IMF on other carcasses that employ the same equipmentand scanning procedures.

This approach was validated using a total of nine plant scanningsessions in which approximately 80 carcasses were scanned during eachsession. At the end of the data collection period, a total of 671carcasses had been scanned that included harvest facility, scanningdate, images, marbling score and chemical fat data. The data issummarized in Table 1. Groups 2-7 were used in the development of whatis referred to herein as the IMF prediction model, and groups 1, 8, and9 were used as validation groups.

TABLE 1 No. Carcass Harvest Scanning Observations Model Model OutliersDate Group after Edits Development Validation Detected A 1 75 Yes 0 B 276 Yes 0 C 3 21 Yes 0 D 4 79 Yes 1 E 5 79 Yes 0 F 6 89 Yes 0 G 7 73 Yes2 H 8 74 Yes 1 I 9 73 Yes 3

The preliminary analysis from various texture parameters may beperformed by calculating correlation and cross-correlation coefficientsand their significance levels (p values). Table 2 presents an example ofsuch results for parameters that have been observed to show significantcorrelation with chemical IMF values, using 639 carcasses. In theresults below, the determined IMF is the intramuscular fat from theloineye samples as determined by chemical extraction. The parameterspresented in the table are defined as follows:

p1=Fourier intensity coefficient of variation (standard deviationdivided by mean);

p2=Ratio of Fourier powers within normalized freq range of [0.01, 0.50]and [0.51, 1.00];

p3=Ratio of Fourier powers within normalized freq range of [0.01, 0.30]and [0.31, 1.00];

p7=ROI pixel grey scale histogram skewness;

p16=ROI pixel grey scale histogram standard deviation;

p17=ROI pixel grey scale histogram coefficient of variation; and

IMF=intramuscular fat from the loin eye samples as determined bychemical extraction.

TABLE 2 IMF p1 p2 p3 p7 p16 p17 IMF 1.00 p1 0.34 1.00 p2 0.21 0.87 1.00p3 0.19 0.94 0.94 1.00 p7 −0.35 −0.52 −0.43 −0.39 1.00 p16 −0.26 −0.220.16 −0.05^(a) −0.06^(b) 1.00 p17 −0.41 −0.93 −0.74 −0.80 0.58 0.36 1.00^(a)p = 0.2356, ^(b)p = 0.1577, all others have p value < 0.0001

Table 3 presents correlation results for wavelet and Fourier parametersusing ultrasound scans from 69 live pigs and chemical IMF. The waveletbased parameters presented in the table are:

W1, W2, W3=Energy in the three high-pass sub-bands for level-1 waveletdecomposition;

W4, W5, W6=Energy in the three high-pass sub-bands for level-2 waveletdecomposition; and

W7, W8=Energy in the upper two high-pass sub-bands for level-3 waveletdecomposition.

TABLE 3 IMF P1 P2 P3 P4 W1 W2 W3 W4 W5 W6 W7 W8 IMF 1.00 P1 0.38 1.00 P20.22 0.94 1.00 P3 0.28 0.97 0.96 1.00 P4 0.47 0.95 0.83 0.89 1.00 W1−0.11 0.16 0.30 0.13 0.09 1.00 W2 −0.19 0.07 0.22 0.06 −0.01 0.90 1.00W3 −0.12 0.17 0.33 0.16 0.08 0.97 0.92 1.00 W4 −0.20 0.07 0.24 0.07−0.03 0.96 0.94 0.98 1.00 W5 −0.23 0.11 0.25 0.13 0.01 0.67 0.90 0.750.77 1.00 W6 −0.11 0.17 0.32 0.17 0.07 0.86 0.90 0.95 0.92 0.82 1.00 W7−0.17 0.10 0.24 0.11 0.00 0.73 0.83 0.83 0.85 0.82 0.93 1.00 W8 −0.230.14 0.25 0.17 0.05 0.46 0.70 0.56 0.56 0.91 0.67 0.71 1.00

The final regression parameters determined for the IMF prediction modeldeveloped in accordance with the present invention for pork loin arepresented in Table 4.

TABLE 4 Regression Coefficient Texture Parameter Estimate Probability >|t| Statistic b₀, intercept 1.442867943 <.0001 b₁ .107983285 <.0001 b₂.002812736 <.0001 b₃ −.030314266 <.0001 b₇ −.440864806 <.0001 b₁₆−.045328050 <.0001 b₁₇ na

Accordingly, aspects of the present invention provide an unexpectedlyaccurate prediction of relative IMF content using an automatedimage-processing system. The predictive ability is further underscoredby the correlation between the prediction and chemical-based IMFmeasurements. Chemical-based IMF measurements provide an objectivemeasurement that does not rely upon subjective visual measurements.Thus, the ability to use imaging technology to accurately predict achemical measurement allows for the use of noninvasive (e.g.,ultrasound) imaging technology in fully-automated processing systems.

In various embodiments, determining fat depth and loin depth can beimportant for predicting fat-free lean in swine carcasses, and may forman initial step in analyses performed in accordance with the presentinvention. There are different types of methods for fat and depthdetermination, some of which include manual measurements that include10th rib backfat and loin area; using an insertable optical probe;cross-sectional scanning; and ultrasonic scanning. While manual methodshave been observed to be relatively precise, accurate measurementsrequire highly trained technicians and the process is time-consuming andlabor intensive. In accordance with aspects of the present invention,fat depth and muscle depth determinations can be made from longitudinalscans of ultrasound images, and such processes may be automated.

FIG. 9 is an ultrasound image of a swine carcass loineye muscle,captured using an Aloka SSD 500V ultrasound scanner, a 12 cm lineartransducer of 3.5 MHz and a Sensoray 2255S frame grabber. It is alongitudinal image of a swine carcass positioned over the last 3 to 4ribs. The top-most light-grey band is the transducer skin boundary 1.Below this is very thin light grey line which is the skin-fat boundary2. There are further light-grey bands that correspond to three fatlayers and fat-muscle layer boundary 3. The last three ribs, 6, 7, and8, respectively, are clearly seen in the lower half of image as threevertical columns with the intercostales muscles 9 holding the ribs. Themuscle above these ribs is the longissimus dorsi muscle. The boundarybetween the loin eye muscle and the ribs is the rib-muscle boundary 5.

A process for determining the fat depth 4 and loin eye muscle depth 10may be automated for swine carcass data in a real time live-streamingscanning system. The fat depth 4 is the difference between the twoboundary positions, skin-fat 2 and fat-muscle 3; whereas the loin eyemuscle depth 10 is the difference between the two boundary positions,fat-muscle boundary 3 and rib-muscle boundary 5. Exemplary automationalgorithms for fat and loin depth are discussed in detail in thefollowing discussions. The percentage of fat-free lean in pork muscletissue is calculated using the fat depth and loin eye muscle depth asalso discussed below.

Fat depth automation algorithms in accordance with certain embodimentsinclude determining the two boundary positions, skin-fat and fat-muscle,from the ultrasound image of a swine carcass. FIG. 10A shows a blockdiagram of an example fat depth automation algorithm, which includesdetermining the fat-skin boundary, determining the fat-muscle boundary,and calculating the fat depth.

Threshold-based operations are used on the captured ultrasound imagebased on the horizontal resolution of grey level intensity to determinethe desired boundary positions. First, the sum of grey level intensityalong each row (horizontal resolution) and the entire image width(typically 640 pixels) is calculated. The sum is normalized with respectto the maximum of sum value. The row corresponding to a maximum value isthe transducer-skin boundary. The intensity sum is scanned startingafter a set number of pixel rows (e.g., 10) from the transducer-skinboundary until the end of the rows for the skin-fat boundary. A row withits intensity greater than a predefined threshold (e.g., 0.6) with achange in slope is determined. This row corresponds to the skin-fatboundary.

An image intensity histogram mean may be computed for sliding imagestrips of a predefined height (e.g., 13 pixels) and width that is thesame as the actual tissue area (e.g., 500 pixels), for example, movingacross the rows from the skin-fat boundary to bottom with a step sizeequal to half the strip height (e.g., 6 pixels). The starting row ofeach sliding image strip and its corresponding histogram mean are storedin an array. The strips corresponding to approximately 30 mm region(e.g., strips 1 to 25) covering the upper half of an image are processedfurther and the strip having a local maximum greater than a specificthreshold (e.g., 0.8), and with a change in slope, is determined. Assuch, the selected strip should have the highest histogram mean greaterthan the threshold in this region, and this value should be higher thanits consecutive previous and next strips. All the possible strips(1/2/3) corresponding to the three fat layers, satisfying the predefinedthreshold and change of slope criteria, are determined and combined in agroup. The starting row of the last strip in this group corresponding tothe third fat layer is assigned as the average row position for thefat-muscle boundary position. Fine adjustments are performed on thisboundary position to get the closest fat-muscle boundary in the regionbetween different pairs of ribs, at the same location as that of theloin depth measurements.

The fat depth may then be calculated as the difference between the twoboundaries corresponding to skin-fat and fat-muscle. This difference isdivided by a pixel to mm conversion ratio (e.g., 1 mm to 3.94 pixels)for the given equipment setting. There is also a difference inultrasound velocities for the fat (e.g., 1430 m/s) and the scanner(e.g., 1540 m/s), and thus an adjustment factor may also be applied bymultiplying the ratio of the velocities (e.g., 0.92857) by thecalculated depth. For the values given above, the final fat depthformula is:

Fat depth=((Fat-muscle boundary row−Skin-fat boundary row)/3.94)*0.92857

An example algorithm for loin depth measurement proceeds as illustratedin the block diagram in FIG. 10B. First, the rib column positions forthe first two ribs (labeled 6, 7, and 8 in FIG. 9) starting from theleft side of the image are determined. Secondly, the rib top boundariescorresponding to these two rib columns are calculated. Then, these ribtop boundaries are processed for fine adjustment to determine theboundary of the intercostales muscles. Finally, the loin eye muscledepth is calculated using the difference between the fat-muscle and therib-muscle boundaries and proper pixel to mm conversion ratio for theparticular study setup. The depth value is adjusted for a correctionfactor for ultrasound velocity in muscle tissue. An accuracy flag may beassigned to each depth measurement based on the image characteristicsencountered in the algorithm to get the confidence level for themeasurement. Each of these steps is discussed in detail below.

The fat and muscle tissue of swine carcass indicated in an ultrasoundimage takes up only a portion of the image area (e.g., from rows 49 to448 and columns 53 to 502 in a 640×480 pixel image). In a given image, asub-image may be selected and considered for determining rib columnpositions for all the ribs from the left side of the image. Smallsliding vertical strips (e.g., 10 pixels wide) are selected in thesub-image. The grey level intensity average is computed for each slidingstrip. The starting column of each sliding strip and its correspondingintensity average is stored in an array. The array length is equal tothe number of sliding strips in the sub-image.

The computed intensity average for sliding strips across columns is usedto determine the rib column positions for the ribs starting from theleft side of the image. The main focus to measure loin depth is betweena pair of ribs due to the preferable position of image ROI for textureanalysis for the prediction of IMF in the same region. There are someexceptions to this where the image may be dark in this region.

A group of strips starting from the left side of the image (e.g., thefirst 8 strips) from the column intensity average array are consideredto determine the first rib column position. The strip having localminima of the intensity average with a change in slope is determined.The selected strip should have the lowest intensity average in thisrange, and its value should be lower than its consecutive previous andnext strips. The starting column of this selected strip is assigned asthe column position for the first rib. If the desired strip is notfound, the first rib column position is assigned to zero. Since the tworib columns are not closer than approximately 100 pixels (e.g., 25 mm),the previous rib range is advanced by a predefined interval (e.g., 8strips) and used as the range for the next rib. A similar procedure isperformed to find a strip having local minima of the intensity averagewith a change in slope to determine the next rib column position. If thedesired strip is not found, the rib column position is assigned to zero.This procedure is repeated to get all the possible rib column positionsstarting from the left side of the image.

After determining the first and second rib positions, the rowcorresponding to rib top boundary for these two ribs are determined inthe next step. Based on the possibilities of both the rib columns beingzero or non-zero, there are 4 cases for calculating rib top boundaryrows (refer to FIG. 9 for examples of Rib1 and Rib2 positions):

i) Rib1≠10 and Rib2≠0;

ii) Rib1=0 and Rib2≠0;

iii) Rib1≠0 and Rib2=0; and

iv) Rib1=0 and Rib2=0.

In case (i), the rib top boundary is calculated for the two rib columnsusing the process described in detail below. The average of the two ribtop boundary rows is calculated and the algorithm proceeds to the nextstep in order to perform some fine adjustments to get the closestrib-muscle boundary required for measurement of the loin eye muscledepth.

In cases (ii) and (iii), the rib top boundary for the non-zero rib valueis calculated and the algorithm proceeds to the next step of fineadjustment.

In case (iv), the final rib-muscle boundary and loin eye muscle depthare both assigned to zero and an accuracy flag is set to indicateincorrect measurement and exit from the algorithm.

For every non-zero rib column 1 or 2, a sub-image is selected defined bythe row starting from the fat-muscle boundary plus a set number ofpixels, such as 120, to a set final row, such as row 420 (in mmconversion, fat-muscle boundary plus 30 mm to 105 mm). Within thissub-image a small moving image box of a set number of pixels (e.g., 13)high is selected starting from the bottom-most row. The width of thisbox is a set number of pixels (e.g., 30) covering the area in thevicinity of the respective rib column. The grey level intensity averageof this image box is calculated. The image box is moved upwards alongthe rows with a step size of a set number of pixels (e.g., 6) and theintensity average is computed for all the image boxes in this sub-image.The starting row of each image box and its corresponding intensityaverage values are stored in an array. The box having local maxima ofthe intensity average with a change in slope is determined for therespective rib column. The starting row of this selected box is assignedto the rib top boundary position for the respective rib. If the desiredbox is not found, the rib top boundary position is assigned to thestarting row of the local maxima irrespective of change in slopecriteria. This procedure is performed for all non-zero rib columnpositions to determine respective rib top boundary positions.

In the next step, fine adjustments may be performed on the rib topboundary rows to obtain the closest row position near the rib-muscleboundary for the loin eye muscle depth. For example, the intercostalesmuscles area between the ribs is processed to get the closest point ofthe rib-muscle boundary. First, the average of rib top boundary rows fornon-zero rib columns is computed. There are three possible cases forcolumn range to perform fine adjustment based on rib column values withthe step equal to a set number of pixels (e.g., 15) as below:

-   -   i) If (Rib1≠0 and Rib2≠0), then the column range is from        (Rib1−step) to (Rib2+step).    -   ii) If (Rib1≠0 and Rib2=0), then the column range is from        (Rib1−step) to (Rib1+step).    -   iii) If (Rib1=0 and Rib2≠0), then the column range is from        (Rib2−step) to (Rib2+step).        Once the column range is decided, the row range for fine        adjustment is selected to the region with row position starting        from average rib top boundary minus a set number of pixels        (e.g., 35) to average rib position plus a set number of pixels        (e.g., 30) which is around 8 mm up and down from the average rib        top boundary. Then, starting from the top row, a small image        strip (e.g., 8 pixels height and width equal to the column        range) is considered and its average grey level intensity is        computed. The strip is moved down (e.g., using a 4 pixel step        size) until the bottom row is reached, and the same computation        is performed for all the strips. The starting row of each image        strip and its corresponding intensity average values are stored        in an array. The difference between the intensity average values        for each strip with its next consecutive strip is calculated.        The starting row of the strip with the lowest negative        difference is assigned to the final rib-muscle boundary row        position required for the loin eye muscle depth measurement. If        the desired strip is not found, the final rib-muscle boundary is        assigned to the average rib-top boundary. This boundary        corresponds to the top interface of the intercostales muscles.

To determine the bottom interface of the intercostales muscles, the rowrange is selected as the region with row position starting from therib-muscle boundary plus a set number of pixels (e.g., 24) to therib-muscle boundary plus a set number of pixels (e.g., 70) which isapproximately 18 mm down from the rib-muscle boundary. The column rangeis the same as the one used for fine adjustment of the rib-muscleboundary. Then, starting from the top row, a small image strip (e.g., 13pixels height and width equal to the column range), is considered, andits average grey level intensity is computed. The strip is moved down(e.g. using a 6 pixels step size) until the bottom row is reached, andthe same computation is performed for all the strips. The starting rowof each image strip and its corresponding intensity average values arestored in an array. The strip having local maxima of the intensityaverage with a change in slope is determined. The starting row of thisselected strip is assigned to the bottom interface of the intercostalesmuscles. If the desired strip is not found, this boundary position isassigned to the starting row of the local maxima irrespective of thechange in slope criteria. The user has the flexibility to measure theloin depth at a preferred location with respect to the intercostalesmuscles and the ribs. For example, one can measure loin depth up to therib-muscle boundary (top interface of the intercostales muscles) or tothe bottom interface of the intercostales muscles between any of the ribpairs.

For fine adjustment of the fat-muscle boundary, the row range for fineadjustment is selected as the region with row position starting from theaverage fat-muscle boundary minus a set number of pixels (e.g., 24) tothe average fat boundary plus a set number of pixels (e.g., 24). This isaround 6 mm up and down from the average fat-muscle boundary. The columnrange is the same as the one used for fine adjustment of the rib-muscleboundary. Then, starting from the top row, a small image strip (e.g., 13pixels height and width equal to the column range), is considered, andits average grey level histogram mean is computed. The strip is moveddown (e.g., using a 6 pixel step size) until the bottom row is reached,and the same computation is performed for all the strips. The startingrow of each image strip and its corresponding histogram mean values arestored in an array. The difference in histogram mean values for eachstrip with its next consecutive strip is calculated. The starting row ofthe strip with the highest positive difference is assigned to the finalfat-muscle boundary row position required for the fat depth measurement.If the desired strip is not found, the final fat-muscle boundaryposition is assigned to the average fat-muscle boundary.

Once the required rib-muscle and fat-muscle boundary positions aredetermined, the next step calculates the loin eye muscle depth based onthe two boundary positions. An accuracy flag may also be assigned toindicate measurement accuracy. The loin eye muscle depth is thedifference between the two boundaries corresponding to fat-muscle(determined in fat depth automation algorithm) and rib-muscle from theprevious step. This difference is divided by the pixel to mm conversionratio (e.g., 1 mm to 3.94 pixels) for the particular setup. For example,the final loin depth formula is: Loin eye muscle depth=((Fat-muscleboundary row−rib-muscle boundary row)/3.94)*1.025974.

In some cases, incorrect measurement for the loin eye muscle depth mayresult, for example due to high contrast, dark images, high echoes,unclear or deep down ribs, and blur that may cause false decisions onrib column position, rib top boundary row, and fine adjustment ofrib-muscle boundary. Hence, an accuracy flag may be assigned to eachmeasurement to indicate a confidence level. The flag may be assigned to‘0’ for correct and ‘1’ for incorrect (or high probability of incorrect)measurement. This flag may be set to 1 based on the imagecharacteristics encountered across the algorithm and are listed below:

-   -   i) Rib1=0 and/or Rib2=0    -   ii) Rib-muscle boundary=0    -   iii) Rib-muscle boundary≧420 (i.e., last allowable line)    -   iv) (Rib1−Rib2)>200 (i.e., largest allowable difference)    -   v) (Rib1 top−Rib2 top)≧40 (i.e., largest allowable difference)    -   vi) image histogram mean<45

The fat depth and loin eye muscle depth may be used to predict thepercentage of fat-free lean in pork muscle tissue. The National PorkProducers Council has published six different equations for predictingfat-free lean based on the fat and muscle depth measurement system(NPPC, 2001). The equation given below calculates the percent fat-freelean based on the ultrasound fat and loin depth measurements.

Perc_lean=((15.31+(0.51*hot carcass weight)+(((3.813*loindepth)−(31.277*fat depth))/25.4))/hot carcass weight)*100

The number and diversity of the various embodiments show the surprisingversatility and effectiveness of the devices and methods associated withembodiments of the present invention. For instance, the surprisingeffectiveness and accuracy of the developed image processing algorithmsfacilitates usage in a variety of applications and environments. Inanother instance, the flexibility to apply filters to the data andalgorithms provides a surprisingly robust and efficient solution to anumber of different problems. Thus, the embodiments disclosed hereinshould not be viewed as limiting and should be recognized as providingsupport for a variety of variations and related applications.

One such application relates to a method of assessing tissuecharacteristics or attributes in a portion of muscle tissue. The methodincludes selecting a region of interest within an image of the portionof muscle tissue; applying image texture processing to the region ofinterest; and extracting, responsive to the image texture processing,tissue characteristics or attributes of the portion of muscle tissue.The step of selecting a region of interest within an image of theportion of muscle tissue can include the use of fat and loin depthmeasurements and/or rib boundaries. In some instances, a set of textureparameters derived from images of the portion of muscle tissue can beused in combination with a prediction formula.

Other applications relate to one or more of the following. Regressionmodeling, statistical editing or pass filter can be used in accordancewith any embodiments of the present invention. Images can be filteredbased upon one or more of pressure sensing, histogram thresholding,grey-scale gating, reflection intensities, blurriness, contrast levels,undesirable echo artifacts, and electromagnetic interference. Systems,algorithms or parameters can be normalized across a variety of devicesand components. Automated positioning systems can be used for placementof an image probe/sensor on a portion of muscle tissue in accordancewith a variety of embodiments. Different portions of muscle tissue canbe sorted as a function of determined characteristics for portions ofmuscle tissue. The devices, methods, systems or arrangements of variousembodiments of the invention can be applied to live animals, which canbe useful for determining animal yield and quality calculations for theanimals.

Aspects of the present invention lend themselves to implementations in avariety of devices including, but not limited to, hardware circuitry,programmable logic devices, firmware, software, and combinationsthereof. A specific example includes computer readable medium storingcomputer executable instructions that when executed by a processorperform one or more of the process steps. The implementations of thevarious algorithms and methods describe herein effectively transformswhat would otherwise be a general purpose processor into aspecially-programmed processor that is configured and arranged toimplement the specialized algorithms.

It should be apparent that the various methods and algorithms discussedherein represent more than abstract concepts and mental steps. Forinstance, embodiments of the present invention relate to thetransformation of specific image-based content and include hardwareinterfaces with various input and output devices.

While the present invention has been described above and in the claimsthat follow, those skilled in the art will recognize that many changesmay be made thereto without departing from the spirit and scope of thepresent invention. Such changes may include, for example, adjustments tovarious parameters or applications other than pork.

1. A method of measuring the relative content of intramuscular fat (IMF) in a portion of muscle tissue of a food animal, the method comprising: to the portion of muscle tissue, presenting a probe for carrying a response-provoking signal; and measuring, in response to the response-provoking signal, the relative content of IMF in the portion of muscle tissue as a function of the pressure being exerted between the probe and the portion.
 2. A method of claim 1, further including the step of sensing the pressure being exerted between the probe and the food animal.
 3. A method of claim 1, further including the step of using the measured IMF content to perform at least one of selecting livestock for breeding or rating the muscle tissue based on quality criteria and wherein the aforementioned steps are used to calibrate equipment used in performing the method.
 4. A method of claim 1, further including repeating the steps of presenting and measuring on subsequently processed muscle tissue, wherein the steps of repeating, presenting and measuring are implemented automatically and without human intervention.
 5. A system for measuring the relative content of intramuscular fat (IMF) in a portion of muscle tissue within a food animal, the system comprising: a probe for carrying a response-provoking signal to the portion of muscle tissue; a pressure sensor for sensing pressure being exerted between the probe and the food animal; and a data processor to measure the relative content of IMF in the portion of muscle tissue, wherein the relative content of IMF is determined as a function of the response-provoking signal and the sensed pressure.
 6. A device assessing characteristics of a portion of muscle tissue from a food animal, comprising: an image sensor for imaging the portion muscle tissue; an image processing arrangement for generating image parameters from images obtained from the image sensor and for generating an assessment of characteristics of the portion of muscle tissue; and a user interface for allowing human interaction with the device.
 7. The device of claim 6, further including pressure sensors for detecting pressure exerted between the food animal and the image sensor.
 8. The device of claim 6, wherein the image sensor is an ultrasound imaging probe having pressure sensors for detecting pressure exerted between the food animal and the image sensor and having indicators that alert an operator of the current amount of pressure being detected.
 9. The device of claim 6, further including one or more of a set of light emitting diodes (LEDs), a touch screen, an emergency-stop switch, a water-tight enclosure that contains electronics of the device, operator video display, carcass identification interface, a data transfer device and a data storage device.
 10. A method of assessing tissue characteristics or attributes in a portion of muscle tissue, the method comprising one or more of the following steps: capturing a set of image frames of the portion of muscle tissue; performing frame editing or selection of the captured sets of image frames; performing automatic image quality detection; determining fat thickness and muscle depth; applying image texture processing to one or more of the set of image frames; and extracting, from the set of image frames, tissue characteristics or attributes of the portion of muscle tissue.
 11. A method of assessing tissue characteristics or attributes in a portion of muscle tissue, the method comprising: selecting a region of interest within an image of the portion of muscle tissue; applying image texture processing to the region of interest; extracting, responsive to the image texture processing, tissue characteristics or attributes of the portion of muscle tissue.
 12. The method of claim 11, wherein the step of selecting a region of interest within an image of the portion of muscle tissue includes the use of fat and loin depth measurements and/or rib boundaries.
 13. A system for measuring the relative content of intramuscular fat (IMF) in a portion of muscle tissue of a food animal, the system comprising: a probe configured and arranged to carry an ultrasound signal to the portion of muscle tissue; a mechanism configured and arranged to guide the probe to a desired location on the animal; an image sensor configured and arranged to generate image data from the ultrasound signal; a pressure sensor configured and arranged to sense pressure being exerted between the probe and the animal; and a data processor configured and arranged to measure the relative content of IMF in the portion of muscle tissue by processing the image data as a function of the sensed pressure.
 14. The system of claim 13, wherein the data processor is further configured and arranged to identify a region of interest within the image data and to measure the relative content of IMF from within the region of interest.
 15. The system of claim 13, wherein the image sensor is further configured and arranged to capture a plurality of image frames and wherein the data processor is further configured and arranged to select a set of image frames from the plurality of image frames as a function of image parameters for each image frame of the plurality of image frames, and to measure the relative content of IMF using a combination of IMF-based calculations from respective frames of the set of image frames.
 16. The system of claim 13, further including indicators that alert an operator to the current amount of pressure being sensed by the pressure sensor.
 17. The system of claim 13, further including one or more of a set of light emitting diodes (LEDs), a touch screen, an emergency-stop switch, a water-tight enclosure that contains electronics of the device, a video display, carcass identification interface, a data transfer device and a data storage device.
 18. The system of claim 13, wherein the data processor is further configured and arranged to perform automatic image quality detection for a set of image frames captured by the image sensor, to determine fat thickness and muscle depth of the portion of muscle tissue, to apply image texture processing to one or more of the set of image frames, to extract, from the set of image frames, tissue characteristics of the portion of muscle tissue, and to use the extracted tissue characteristics to measure the relative content of IMF in the portion of muscle tissue.
 19. The system of claim 13, wherein the processor is further configured and arranged to select a region of interest within an image of the portion of muscle tissue by identifying rib boundaries and automatically determining fat and loin depth measurements from the image data.
 20. The system of claim 13, wherein the processor is further configured and arranged to measure the relative content of IMF in the portion of muscle tissue using parameters that include Fourier intensity coefficient of variation, a ratio of Fourier powers within a first normalized freq range, a ratio of Fourier powers within a second normalized freq range, pixel grey scale histogram skew value, pixel grey scale histogram standard deviation and pixel grey scale histogram coefficient of variation.
 21. A method of measuring percent lean in a food animal, the method comprising: presenting an ultrasound signal to a tissue region of the food animal using a probe; capturing image data corresponding to one or more image frames of the tissue region from the ultrasound signal; performing filter-based editing of the captured one or more image frames; determining image-based landmarks and tissue interfaces from captured one or more image frames; calculating fat depth and muscle depth parameters by applying a statistical filter to the one or more image frames; and measuring the percent lean in the food animal as a function of the calculated fat depth and muscle depth parameters using a pressure sensor based filter and a statistical filter. 